You use approximations to visualize the behavior of your data (data trends) and to predict new values of your output parameters based on a specified combination of values of your input parameters, where that combination does not already exist in your data set. The approximation table summarizes information about a selected approximation. Output information is displayed in a table, with each column corresponding to an output predicted by the approximation object. The rows describe upper and lower bound values, the algorithm used, error measures describing the training process, and error measures describing the validation process associated with each predicted output. In the inputs section, the rows describe the upper and lower bounds for each input, and which outputs an input was used to generate. Checks in the output parameter rows indicate that the input described by the column was used to generate the output indicated by the row. | ||||||||||